A multi-scale convolutional neural networks based on attention mechanism for motor imagery classification

Ruifeng Zhang; Jinjie Bi; Renhui Huang; Shoulin Huang; Tinghui Li

doi:10.1117/12.3012218

21 December 2023 A multi-scale convolutional neural networks based on attention mechanism for motor imagery classification

Ruifeng Zhang, Jinjie Bi, Renhui Huang, Shoulin Huang, Tinghui Li

Author Affiliations +

Proceedings Volume 12970, Fourth International Conference on Signal Processing and Computer Science (SPCS 2023); 129702B (2023) https://doi.org/10.1117/12.3012218
Event: Fourth International Conference on Signal Processing and Computer Science (SPCS 2023), 2023, Guilin, China

Abstract

Convolutional neural networks (CNNs) applied in motor imagery brain-computer interface (MI-BCI) can employ large network sizes to learn rich information contained in electroencephalography (EEG) signals for achieving high classification performance. However, the large-size networks lead to high computational resources and long training time so that hindering CNNs in the practical application of BCIs. To address this issue, we propose a multi-scale convolutional neural network with attention mechanism (MS-CNN) in this study. The proposed model has the following properties: 1) it optimizes a multi-scale architecture for learning the spatial-spectral information related to MIs directly, avoiding the extra step of feature extraction. 2) it utilizes attention mechanism for efficient information enhancement while keeping a relatively small network size. The MS-CNN achieves the maximum classification accuracy value of 88.46% (±6.54) compared with existing baseline methods of the BCI competition IV 2a dataset.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

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Ruifeng Zhang, Jinjie Bi, Renhui Huang, Shoulin Huang, and Tinghui Li "A multi-scale convolutional neural networks based on attention mechanism for motor imagery classification", Proc. SPIE 12970, Fourth International Conference on Signal Processing and Computer Science (SPCS 2023), 129702B (21 December 2023); https://doi.org/10.1117/12.3012218

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KEYWORDS

Feature extraction

Electroencephalography

Education and training

Convolutional neural networks

Data modeling

Convolution

Brain-machine interfaces

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